Open-domain conversational ai may be designed as a simple rule-based template system or may involve complex ann architectures.
Indeed, six approaches are possible: (1) rule-based method, (2) rl that uses rewards to train a policy, (3) adversarial networks that utilize a discriminator and a generator, (4) retrieval-based method that searches from a candidate pool and selects a proper candidate, (5) generation-based method that generates a response word by word based on conditioning, and (6) hybrid method [1]}, [2]}, [3]}.
Certain modern systems are still designed in the rule-based style that was used for ELIZA [1]}.
The ann models are usually trained on large datasets to generate responses, hence, they are data-intensive.
The data-driven approach is more suitable for open-domain conversational ai [1]}.
Such systems learn inductively from large datasets involving many turns in conversations.
A turn (or utterance) in a conversation is each single contribution from a speaker [6]}, [1]}.
The data may be from written conversations, such as the MultiWOZ [8]}, transcripts of human-human spoken conversations, such as the gdc [9]}, crowdsourced conversations, such as the EmpatheticDialogues [10]}, and social media conversations like Familjelivfamiljeliv.se or Redditreddit.com [11]}, [12]}.
As already acknowledged that the amount of data needed for training deep ml models is usually large, they are normally first pretrained on large, unstructured text or conversations before being finetuned on specific conversational data.
